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Google Professional Data Engineer Exam - Topic 3 Question 71 Discussion

Actual exam question for Google's Professional Data Engineer exam
Question #: 71
Topic #: 3
[All Professional Data Engineer Questions]

You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of dat

a. What should you do?

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Suggested Answer: C

by Lucy at Oct 09, 2023, 05:21 AM

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Lettie
5 months ago
Not sure if Cloud Bigtable is the best choice for this use case.
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Tresa
5 months ago
A is solid, but I prefer the simplicity of C.
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Micaela
5 months ago
Surprised that using Cloud SQL is even an option here!
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Noel
5 months ago
I think B is overkill, two models just for filtering?
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Jody
5 months ago
Option C seems the most efficient for quick labeling!
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Avery
6 months ago
I’m a bit confused about whether to use one model or two for filtering. I think having two models could complicate things, but it might be necessary for accuracy.
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Dierdre
6 months ago
I practiced a similar question where we had to choose between different storage options. I feel like Cloud Bigtable is better for handling large datasets like this one.
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Izetta
6 months ago
I'm not entirely sure, but I think using Cloud SQL might slow down the filtering process compared to Cloud Bigtable.
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Detra
6 months ago
I remember we discussed using the Cloud Video Intelligence API in class. It seems like a good fit for generating labels quickly.
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Pura
6 months ago
Whoa, this is a lot to take in. I'm not sure I have the experience to build a full recommendation engine from scratch, especially with all the different Google Cloud services involved. Maybe I'd start by trying to understand the overall architecture and see if I can find any sample code or tutorials to get me started.
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Anna
6 months ago
Okay, I think I've got a plan. I'd start by exploring the Cloud Video Intelligence API and seeing how I can use that to generate the video labels. Then I'd look into Cloud Bigtable as a fast, scalable way to store and filter the data based on customer preferences. I'm feeling pretty confident I can tackle this if I break it down step-by-step.
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Michel
6 months ago
Hmm, this is a tough one. I'm not super familiar with Spark MLlib or Cloud Dataproc, but I think option C might be the way to go. Using the Cloud Video Intelligence API to generate the labels and then storing and filtering the data in Cloud Bigtable sounds like a more straightforward approach.
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Dorian
6 months ago
This looks like a complex problem that requires a lot of different components to be integrated. I'm not sure if I have the skills to build a full solution from scratch, but I think I could at least try to break it down and tackle it piece by piece.
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Johnathon
6 months ago
I'm a bit confused on this one. I know there are a few different objects we could use, but I'm not totally sure which one is the best fit for modeling a client. I'll need to double-check the documentation to make sure I get this right.
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Eliseo
6 months ago
Hmm, I'm a bit confused by this question. It mentions a "series of questions" and that I won't be able to go back and review previous questions. That seems a bit tricky. I'll need to be really careful to understand each question before answering.
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Mel
6 months ago
I think you can't place hosted elements in Elevation Views, but I could be wrong. It's kind of tricky.
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Rima
7 months ago
Okay, let's see. The key here is to determine the appropriate action based on the risk exposure. I think I have a strategy to work through this step-by-step.
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Audra
11 months ago
As a video enthusiast, I'd love to see a recommendation engine that can keep up with my binge-watching habits! Option C sounds like it would be a 'reel' winner.
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Kristel
9 months ago
I think Option C is the way to go for developing an application with a recommendation engine on Google Cloud.
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Taryn
9 months ago
It would definitely help in providing fast filtering suggestions based on past views.
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Fausto
9 months ago
I agree, using the Cloud Video Intelligence API to generate labels and storing data in Cloud Bigtable seems like a solid solution.
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Louvenia
10 months ago
Option C sounds like a great choice for generating labels and filtering suggestions based on customer preferences.
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Stephanie
11 months ago
Hmm, I'm not sure I'd want to build a complex Spark MLlib model just for this task. The Cloud Video Intelligence API in Option C or D looks like a more efficient solution to me.
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Donette
10 months ago
Yeah, I think using the Cloud Video Intelligence API in Option C or D would be the way to go for fast filtering suggestions.
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Salome
10 months ago
I agree, Option D also seems like a good option. Storing data in Cloud SQL could be useful.
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Ocie
10 months ago
Yeah, building and training multiple models in Option B might be overkill. The Cloud Video Intelligence API sounds simpler.
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Quentin
10 months ago
Option C sounds like a good choice. Using the Cloud Video Intelligence API seems efficient.
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Frederic
10 months ago
I agree, Option D also seems like a good option. Storing data in Cloud SQL could be useful.
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Talia
11 months ago
Option C sounds like a good choice. Using the Cloud Video Intelligence API seems efficient.
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Fernanda
12 months ago
Option D seems like a good choice if you're already familiar with Cloud SQL. The simplicity of storing data in a relational database and joining the labels with user history could be a straightforward approach.
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Tonette
10 months ago
That's true, combining the API for labels with Cloud SQL for filtering could be a powerful combination.
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Rashida
10 months ago
But wouldn't using the Cloud Video Intelligence API to generate labels be more accurate?
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Nina
10 months ago
I agree, using Cloud SQL for storing data and joining labels with user history sounds efficient.
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Kenia
11 months ago
Option D seems like a good choice if you're already familiar with Cloud SQL.
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Joana
12 months ago
I'm leaning towards Option B. Having two separate models, one for generating labels and one for filtering, could provide more flexibility and control over the recommendation process.
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Bernardine
11 months ago
I agree, Option B seems like it would give us more control over the recommendation process.
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Frederica
11 months ago
Option B sounds like a good choice. Having separate models for generating labels and filtering could be beneficial.
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Patrick
12 months ago
That's a good point, but I still think option A is more straightforward and easier to implement.
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Lynelle
12 months ago
I disagree, I believe option B is better. Having two classification models for labeling and filtering can provide more accurate results.
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Galen
1 year ago
Option C looks like the most efficient and scalable solution. Using the Cloud Video Intelligence API and Cloud Bigtable seems like a great way to handle the large amounts of data and provide fast filtering capabilities.
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Mary
11 months ago
Yes, using Cloud Bigtable for storing data and filtering predicted labels based on user viewing history is a smart choice for this recommendation engine application.
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Arthur
11 months ago
It's important to have fast filtering suggestions based on customer preferences, and Cloud Bigtable can definitely help with that.
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Jeffrey
11 months ago
I agree, Cloud Video Intelligence API can help generate accurate labels for the videos, and Cloud Bigtable can handle the massive amount of data efficiently.
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Colene
11 months ago
Option C looks like the most efficient and scalable solution. Using the Cloud Video Intelligence API and Cloud Bigtable seems like a great way to handle the large amounts of data and provide fast filtering capabilities.
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Patrick
1 year ago
I think option A is the best choice. Using Spark MLlib for classification and Cloud Dataproc for deployment seems efficient.
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